probability_discount {bayesDP} | R Documentation |
probability_discount
can be used to estimate the posterior
probability of the comparison between historical and current data in the
context of a clinical trial with normal (mean) data.
probability_discount
is not used internally but is given for
educational purposes.
probability_discount(
mu = NULL,
sigma = NULL,
N = NULL,
mu0 = NULL,
sigma0 = NULL,
N0 = NULL,
number_mcmc = 10000,
method = "fixed"
)
mu |
scalar. Mean of the current data. |
sigma |
scalar. Standard deviation of the current data. |
N |
scalar. Number of observations of the current data. |
mu0 |
scalar. Mean of the historical data. |
sigma0 |
scalar. Standard deviation of the historical data. |
N0 |
scalar. Number of observations of the historical data. |
number_mcmc |
scalar. Number of Monte Carlo simulations. Default is 10000. |
method |
character. Analysis method. Default value " |
This function is not used internally but is given for educational purposes. Given the inputs, the output is the posterior probability of the comparison between current and historical data in the context of a clinical trial with normal (mean) data.
probability_discount
returns an object of class
"probability_discount".
An object of class probability_discount
contains the following:
p_hat
scalar. The posterior probability of the comparison historical data weight. If
method="mc"
, a vector of posterior probabilities of length
number_mcmc
is returned.
Haddad, T., Himes, A., Thompson, L., Irony, T., Nair, R. MDIC Computer Modeling and Simulation working group.(2017) Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. Journal of Biopharmaceutical Statistics, 1-15.
probability_discount(
mu = 0, sigma = 1, N = 100,
mu0 = 0.1, sigma0 = 1, N0 = 100
)